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Research Data Management: Data Lifecycle

Data Lifecyle Stages (Brief)

  • Plan & Design
  • Collect & Create
  • Analyze & Collaborate
  • Evaluate & Archive
  • Share & Disseminate
  • Access & Reuse

Data Lifecycle Stages (Explained)

Research Data Management is a continuum of practices. It continues throughout the course of a research project. You will likely jump around and move between phases in the lifecycle, but you should always start at the Plan & Design phase. During the Plan & Design phase, you will need to know: 

  • your research project, 
  • research stakeholders, 
  • roles and responsibilities, 
  • funder requirements, 
  • data goals, 
  • and challenges. 

Use a checklist to help plan and design your work: 

You may need to create a Data Management Plan during this phase. Additional guidance for preliminary steps before creating a plan is available in this guide under the Cost and Compliance tab.

Before launching a research project, design a model for capturing, storing, and organizing your data. Consider project

  • workflows (record data procedures, workflows, protocols, and responsibilities),
  • data types (document what types of data will be produced in the project),
  • metadata standards (use common standards including Common Data Elements or other FAIR metadata standards), 
  • formats (chose non-proprietary formats when available or convert to open, non-proprietary, widely used formats for sharing),
  • volume (understand how much data will be created as part of the project),
  • access (document any specialized tools required).

Design how you will store your data:

Store & Manage is a key component of the Data Lifecycle touching on every stage. Researchers will need to plan for:

  • how data sets will be stored during active working phases (including backups) and for long-term retention,
  • retention polices set by funder or institution,
  • data security,
  • and related costs.

Consider data storage requirements for the project.

  • Cornell Data Storage Finder tool provides general guidance for selecting storage and collaboration options.
  • Contact your campus data liaisons to determine computing and storage options available at your institution.
  • Consider tools that support versioning and collaboration such as GitHub, wikis, and shared drives.

Follow required retention and preservation requirements as established by your institution or funding agency. The Data Curation Network has developed extensive guidance on working with and keeping research data.

Find a repository for sharing and publishing. SUNY offers the following resources, which should be considered first:

  • SUNY Dryad Instance: SUNY Maritime is a members of Dryad and will be experimenting with Dryad as an open data publishing platform and community.  
  • SUNY SOAR https://soar.suny.edu/  is an institutional repository, but not designed for data storage and sharing.

Other repositories available: 

Data publishing repositories should follow FAIR principles https://www.go-fair.org/fair-principles/  

In general, raw data are considered facts and cannot be copyrighted. Community norms for data attribution and scholarly communication are often more successful in documenting origins of data than licensing restrictions when possible.

Data license considerations include the following: 

Data Lifecycle

Image and Biomedical Data Lifecycle created by LMA Research Data Management Working Group at Harvard Medical School licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


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